What Is An AI Engineer?

The short version is that an AI Engineer works on the entire lifecycle of an AI application - that is, an application that utilizes AI at its core. An AI Engineer takes AI models, including Large Language Models, and customizes them to their needs.

(If you want the long version, check out our blog post here)

That requires everything from building models using custom datasets, to training and tuning models, to deploying models and scaling them using cloud technologies.

The role is growing like wildfire, but it's still evolving and will no doubt continue evolving as the AI landscape changes.

What Is AWS SageMaker?

AWS SageMaker (also referred to as Amazon SageMaker) is a fully managed machine learning service that empowers you to quickly build, train, and deploy machine learning models at scale. It eliminates the heavy lifting of infrastructure management, so you can focus on the fun part - creating your own awesome AI projects and applications!

In short, it's one of the leading, real-world AI tools used by AI Engineers, Machine Learning Engineers, Developers and Data Scientists.

Want to know why makes AWS SageMaker really cool though?

It allows end-to-end machine learning in a way that's easy to use, no matter your skill level!

So whether you're a seasoned AI expert or just getting started, SageMaker offers intuitive tools and a user-friendly interface that make machine learning accessible to everyone.

If you're looking to build and deploy your own AI applications, this is the place to be.

Why This Course?

Well, because it's the best, most up-to-date, and practical AI Engineering Bootcamp course online that teaches you real-world AWS SageMaker skills and that lets you get hands-on so that you can actually use your skills in the real-world.

But of course we're biased. So here's a section-by-section breakdown of this AWS SageMaker course so that you can make up your own mind:

1. Introduction: Get an overview of what you'll be doing in this course, meet the other students taking this course, and meet your instructor extraordinaire: Senior Machine Learning Engineer, Patrik Szepesi!

2. Setting Up Our AWS Environment and Best Practices: We'll dive right away into establishing a solid foundation by setting up an AWS account, configuring IAM roles for security, and applying best practices. You'll also learn to set up the AWS SageMaker domain, adjust UI settings, and understand the pricing structures within SageMaker Studio.

3. A Gentle Introduction to HuggingFace in Amazon SageMaker: You'll then get a practical introduction to integrating HuggingFace with AWS SageMaker. The section covers setting up SageMaker with PyTorch and deploying pre-built HuggingFace models for tasks like sentiment analysis, including considerations for autoscaling.

4. Gathering a Dataset for Our Multiclass Text Classification Project: Time to get your hands dirty and learn how to source and prepare datasets for a multiclass text classification task. You'll learn about acquiring datasets, creating S3 buckets, and uploading data effectively to AWS.

5. Exploratory Data Analysis: In this section you'll engage in exploratory data analysis to uncover insights from your datasets, including learning about data visualization techniques and best practices to better understand underlying patterns that could influence subsequent modelling decisions.

6. Setting Up Our Training Notebook: This practical section helps you set up your training environments in Amazon SageMaker, including configuring notebooks and scripting with Python for HuggingFace estimators. This setup is crucial for efficient model training and iteration!

7. Introduction to Tokenizations and Encodings: This is such a cool section. You'll explore the essential NLP (Natural Language Processing) processes of tokenization and encoding, which are fundamental to AI models like LLMs (Large Language Models). The lessons include hands-on activities for creating notebooks, understanding tokenization vocabulary, and practical examples of applying these techniques in model training.

8. Setting up Data Loading with PyTorch: Here you'll learn to create dataset loader classes and configure PyTorch DataLoader, which are key components for handling large volumes of data efficiently during model training.

9. Choose Your Path: Time for another brief interlude! This will prepare you for the advanced topics ahead, helping you decide the path you'll take in learning about the deeper technical details of machine learning and AI models. You can decide if you want the deep dive into mathematics or not (we know some of you don't love the math, and that's okay!).

10. Mathematics Behind Large Language Models and Transformers: This extensive section delves into the mathematical foundations and workings of Transformers and Large Language Models, including multi-head attention, positional encodings, and the theory behind attention mechanisms.

11. Customizing our Model Architecture in PyTorch: Learn to customize a DistilBERT model by adding dropout, linear layers, and ReLU activations. This customization is key for tailoring the model to specific classification tasks and datasets.

12. Creating the Accuracy, Training, and Validation Function: This section focuses on creating accurate, robust training and validation functions to ensure models are evaluated properly and perform well under various conditions.

13. Optimizer Functions, Model Parameters, Cross Entropy Loss Function: Understand and implement optimization functions, adjust model parameters, and delve into the mathematics of the cross entropy loss function, which is essential for training effective machine learning models.

14. Possible Resource Limit Errors Before Training and Deployment: Preparing for potential issues, this section teaches you how to handle resource limit errors by managing and requesting AWS quota increases.

15. Starting Our Training Job and Monitoring it in AWS CloudWatch: This is getting exciting! Because here you'll start your training jobs in SageMaker and learn to monitor and debug these jobs using AWS CloudWatch, gaining insights into the health and performance of your models.

16. Deploying our Multiclass Text Classification Endpoint in SageMaker: This critical section covers the deployment of the trained multiclass text classification model as an Amazon SageMaker endpoint, ensuring you understand the steps to make your models available for real-world applications.

17. Load Testing Our Machine Learning Model: Just because a model is live, doesn't mean it's going to work! You need to perform load testing on your deployed models to evaluate performance and scalability, preparing them for robust and efficient real-world deployments.

18. Production Grade Deployment of Our Machine Learning Model: This section takes you through the steps of deploying your model at a production scale, including setting up AWS Lambda functions and API Gateway, and testing the deployment with tools like Postman. This is where the rubber really meets the road!

19. Cleaning Up Resources: Last but not least, we need to clean things up to make sure you're using the real-world best practices! This section emphasizes the importance of cleaning up AWS resources to manage costs and maintain an efficient cloud environment.

What Careers Does This Course Prepare Me For?

AI and machine learning are so hot right now. If you want to catch and ride the AI wave, SageMaker is a great place to start. It's a skill that's used in a ton of in-demand careers that are at the forefront of Artificial Intelligence including:

AI Engineer & Machine Learning Engineer: Focuses on designing, developing, and customizing machine learning models and deploying them to production environments. Requires skills in model training, optimization, and deployment.

AI Specialist: Specializes in building applications using artificial intelligence technologies and machine learning models.

Data Scientist: Involves analyzing and interpreting complex data to help companies make informed decisions. Requires expertise in data preparation, exploratory data analysis, and model building.

AI Research Scientist: Conducts research to advance the field of artificial intelligence and machine learning. Requires deep understanding of advanced machine learning concepts, including attention mechanisms and large language models.

Cloud Engineer: Focuses on designing, planning, managing, maintaining, and supporting cloud computing applications. Requires knowledge of AWS services and best practices for cloud deployment.

DevOps Engineer: Bridges the gap between development and operations by automating the process of software delivery and infrastructure changes. Needs skills in deploying and monitoring machine learning models using tools like AWS CloudWatch.

Software Engineer: Involves developing software applications, including those with integrated machine learning components. Requires understanding of integrating machine learning models into applications and ensuring their scalability and performance.

Data Engineer: Focuses on building and maintaining data pipelines, ensuring data is clean, reliable, and ready for analysis. Requires knowledge of data storage solutions like AWS S3 and data preparation techniques.

Technical Product Manager: Manages the development and deployment of technology products, including those involving machine learning. Requires an understanding of the technical aspects of machine learning deployment and monitoring.

What Else Should I Know?

By becoming a ZTM member you'll not only get access to all our bootcamp courses, bytes, and projects.

But you’ll also get to join our exclusive live online community classroom to learn alongside thousands of students, alumni, mentors, TAs and Instructors.

Most importantly, you'll be learning from an industry professional (Patrik) that has actual real-world experience as an AI & Machine Learning Engineer. He teaches you the exact strategies and techniques he uses in his role.

Finally, as with all ZTM courses, this course is a living thing. It will be constantly updated as the landscape changes so you can use it as your go-to guide for using Amazon SageMaker now and throughout your career.

Join 1,000s of Zero To Mastery graduates that have gotten hired and are now working at companies like Google, Tesla, Amazon, Apple, IBM, JP Morgan, Facebook, Shopify + other top tech companies.

They come from all different backgrounds, ages, and experiences. Many even started as complete beginners.

So there's no reason it can't be you too.

And you have nothing to lose. Because you can start learning right now and if this course isn't everything you expected, we'll refund you 100% within 30 days. No hassles and no questions asked.